Delivering Quality Of Services For Media Streaming Networks

Delivering Quality Of Services For Media Streaming In Group Communication Over Mobile Ad Hoc Networks (QASAN)

,Arunkumar Thangavelu, Bhuvaneswari Kandasamy, S.N. Sivanandam

Abstract— The major challenge faced by designers of ad hoc network is the deployment of end-to-end quality-of-service support mechanisms for streaming media services over an adhoc group network. Group-oriented services over large ad-hoc networks has a big impact on the needs of streaming services communication in terms of mobility, quality of service (QoS) support and multicasting. In Ad hoc networks, where such features are not embedded with its architecture, it is necessary to develop QoS multicasting strategies. This paper focuses on the basic building blocks of an ad hoc group communication scheme, which achieves multicasting optimal QoS efficiency OptiQ_Policy algorithm by tracking resource availability in a node’s neighborhood based on resource reservations, which announces the required QoS before each session initiation.

The primary quality of service (QoS) issues such as required bandwidth, message delay, traffic type and hop count per route improves the efficiency of streaming services over ad-hoc network. Streaming services support voice, data and video traffic by assessing and adjusting for various levels of QoS. The performance analysis is performed on functional prototype of QASAN ad hoc mobile wireless network with emphasis on service satisfaction for multiple group conference sessions.

The performance of QASAN network is well compared with QoS-aware versions of AODV [20] and TORA[17], well-known ad-hoc routing and limited QoS protocols. Using the SPRUCE[23] bandwidth traffic gathering tool, with a set of C++ modules an extensive set of performance experiments were conducted for these protocols with QASAN on a wide variety of mobility patterns and reservation strategies.

Index Terms —Quality of Services, Streaming Services, Ad Hoc Networks, group communication.

I. INTRODUCTION

QoS (Quality of Service) routing is a key network function for the transmission and distribution of digitized audio / video across next-generation high-speed networks[19]. It has two main objectives : (i) identifying routes that satisfy the QoS constraints; (ii) making efficient use of network resources. The complexity involved in the networks may require the consideration of multiple constraints to make the optimal QoS routing decision.

Most QoS protocols [6],[8],[12] proposed today purely function on best effort basis with no attempt to provide any QoS whatsoever. Routing can inform a source node of the bandwidth and QoS availability of a destination node. The notion of QoS is guarantee provided by network to satisfy a set of predetermined service performance constraints for the user in terms of the end-to-end delay statistics, available bandwidth, probability of packet loss and call admission delay.

When mobility grows high, the established routes are susceptible to link failures, diversions, or decrease of throughput. Thus, absolute throughput or delay bounds are hard to guarantee hence, some researchers have proposed the notion of Soft QoS [2] to achieve quality. Soft QoS discusses on the transient periods of time after the connection set up, where there may exist delay when QoS specification is not honoured. The level of QoS satisfaction is quantified by the fraction of total disruption time over the total connection time. This ratio should be higher than a specified threshold [1].

Algorithms that provide QoS support in AdHoc neworks should include the following features: (i) accurate measurement of bandwidth availability in the shared wireless channel; (ii) accurate measurement of effective end-to-end delay in an unsynchronized environment; (iii) distributed routing algorithm that adapts with the dynamic environment; (iv) resource reservation that guarantees the available resources; (v) efficient resource release upon route adjustment; (vi) instant QoS violation detection and (vii) fast and efficient route recovery.

The task of QASAN is to identify an optimal QoS level which may be possed by a suitable path through the network or route, between the source and destination(s) that will have the necessary resources to meet the service expected by user. The task of resource request, identification, and reservation is the other indispensable ingredient of QoS. The focus of this paper is on identifying optimal QoS limit set for each session based on type of service in use. This is a complex and difficult issue because of the dynamic nature of the network topology and generally imprecise network state information. Throughout this paper the need for optimal QoS OptiQ_Policy for On-Demand routing service in AdHoc networks is stressed.

The remainder of this paper is organised as follows: In Section II, related works in this area are summarized. In Section III, need for optimal QoS for Ad hoc On-demand Service Group Management scheme (QASAN) is discussed. Section IV, focuses on Policy Manager and Negotiation procedures, while Section V, discusses the experimental setup procedures and and its performance analysis based on traffic rate. Finally summary and future work are presented in Section VI.

II. RELATED WORK

A. Review Stage

More and more multimedia data are being transmitted via wireless media, where such applications require diverse QoS. Due to the intrinsic scarcity of wireless bandwidth, it is challenging to provide diverse QoS while achieving high bandwidth utilization. For example, a system may allocate higher bandwidth for multimedia applications to satisfy their QoS at the expense of rejecting new calls that require less bandwidth. In order to enhance bandwidth utilization while satisfying the QoS of existing connections, numerous approaches have been proposed.

A graceful degradation mechanism is proposed by Singh [1] to increase bandwidth utilization by adaptively adjusting bandwidth allocation according to the user-specified loss profiles. For most multimedia applications (e.g., voice, video telephony or video conferencing), service can be degraded in case of congestion as long as it is still within the pre-specified tolerable range. A generic video telephony may require over 40 Kbps but low-motion video telephony requiring about 25 Kbps is acceptable [18]. Thus, a system could free some channels for new calls by lowering the QoS levels of ongoing calls. Chen et al . [14] proposed an optimal degradation strategy by maximizing a revenue function. Sherif et al . [22] proposed an adaptive resource allocation algorithm to maximize bandwidth utilization and tried to achieve fairness with a generic algorithm.

Kwon et al . [9] derived a degradation period ratio under the assumption that the degradation probability and mean degradation time are kept intact in all degradation states. However, these metrics are dependent on the degradation state in which a given call resides, and hence, derive a new degradation ratio. Moreover, it is shown numerically that the degradation ratio does not suffice to reflect the QoS guarantees given to individual calls. Frequently switching among the different degradation levels may be even worse than a large degradation ratio [10]. So, we also derive a formula for switching QoS levels.

Another important issue in wireless communication is the forced-termination (or call dropping) due to non-provisioning of expected QoS probability. In case of shortage of bandwidth, hand-off calls may be dropped, thus compromising their QoS. In order to prevent ongoing calls from potential dropping/termination, Lin et al. [11] gave priority to hand-off calls over new calls, such that the forced-termination probability is improved without seriously degrading the blocking probability of new calls. Naghshineh et al. [15] proposed a distributed call admission control scheme by estimating the possible number of hand-off calls from adjacent cells.

Various reservation-based admission control schemes have also been proposed to reduce the probability of terminating ongoing or hand-off calls [8],[9]. Some optimal solutions subject to different constraints have also been proposed in [13],[24]. Slightly different from the reservation based call admission control (CAC), once the system load exceeds a predefined threshold, we restrict the traffic of newlyinitiated calls so as not to drop hand-off calls.

B. Challenges

One of the major challenges in ad hoc network systems is the deployment of end-to-end quality-ofservice support mechanisms. QASAN focuses on identifying an integrated route discovery, bandwidth provisioning, resource identification, reservation and negotiation on required QoS. QASAN is designed to operate within a TDMA network. Unlike other path finding and route discovery protocols that ignore the impact of the data link layer, QASAN incorporates dynamic session scheduling method which ensures end-to-end QoS for the type of service call.

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